concision-core 0.3.1

/*
    Appellation: format <module>
    Created At: 2026.01.06:14:38:32
    Contrib: @FL03
*/
/// The [`RawModelLayout`] trait defines a minimal interface for objects capable of representing
/// the _layout_; i.e. the number of input, hidden, and output features of a neural network
/// model containing some number of hidden layers.
///
/// **Note**: This trait is implemented for the 3- and 4-tuple consiting of usize elements as
/// well as for the `[usize; 3]` and `[usize; 4]` array types. In both these instances, the
/// elements are ordered as (input, hidden, output) for the 3-element versions, and
/// (input, hidden, output, layers) for the 4-element versions.
pub trait RawModelLayout {
    /// returns the total number of input features defined for the model
    fn input(&self) -> usize;
    /// returns the number of hidden features for the model
    fn hidden(&self) -> usize;
    /// the number of output features for the model
    fn output(&self) -> usize;
    /// returns the number of hidden layers within the network
    fn depth(&self) -> usize;

    /// the dimension of the input layer; (input, hidden)
    fn dim_input(&self) -> (usize, usize) {
        (self.input(), self.hidden())
    }
    /// the dimension of the hidden layers; (hidden, hidden)
    fn dim_hidden(&self) -> (usize, usize) {
        (self.hidden(), self.hidden())
    }
    /// the dimension of the output layer; (hidden, output)
    fn dim_output(&self) -> (usize, usize) {
        (self.hidden(), self.output())
    }
    /// the total number of parameters in the model
    fn size(&self) -> usize {
        self.size_input() + self.size_hidden() + self.size_output()
    }
    /// the total number of input parameters in the model
    fn size_input(&self) -> usize {
        self.input() * self.hidden()
    }
    /// the total number of hidden parameters in the model
    fn size_hidden(&self) -> usize {
        self.hidden() * self.hidden() * self.depth()
    }
    /// the total number of output parameters in the model
    fn size_output(&self) -> usize {
        self.hidden() * self.output()
    }
}
/// The [`RawModelLayoutMut`] trait defines a mutable interface for objects capable of representing
/// the _layout_; i.e. the number of input, hidden, and output features of
pub trait RawModelLayoutMut: RawModelLayout {
    /// returns a mutable reference to number of the input features for the model
    fn input_mut(&mut self) -> &mut usize;
    /// returns a mutable reference to the number of hidden features for the model
    fn hidden_mut(&mut self) -> &mut usize;
    /// returns a mutable reference to the number of hidden layers for the model
    fn layers_mut(&mut self) -> &mut usize;
    /// returns a mutable reference to the output features for the model
    fn output_mut(&mut self) -> &mut usize;

    #[inline]
    /// update the number of input features for the model and return a mutable reference to the
    /// current layout.
    fn set_input(&mut self, input: usize) -> &mut Self {
        *self.input_mut() = input;
        self
    }
    #[inline]
    /// update the number of hidden features for the model and return a mutable reference to
    /// the current layout.
    fn set_hidden(&mut self, hidden: usize) -> &mut Self {
        *self.hidden_mut() = hidden;
        self
    }
    #[inline]
    /// update the number of hidden layers for the model and return a mutable reference to
    /// the current layout.
    fn set_layers(&mut self, layers: usize) -> &mut Self {
        *self.layers_mut() = layers;
        self
    }
    #[inline]
    /// update the number of output features for the model and return a mutable reference to
    /// the current layout.
    fn set_output(&mut self, output: usize) -> &mut Self {
        *self.output_mut() = output;
        self
    }
}

/// The [`LayoutExt`] trait defines an interface for object capable of representing the
/// _layout_; i.e. the number of input, hidden, and output features of a neural network model
/// containing some number of hidden layers.
pub trait LayoutExt: RawModelLayout + RawModelLayoutMut + Clone + core::fmt::Debug {}

/// The [`NetworkDepth`] trait is used to define the depth/kind of a neural network model.
pub trait NetworkDepth {
    private!();

    fn is_deep(&self) -> bool {
        false
    }
}

macro_rules! impl_network_depth {
    ( #[$tgt:ident] $vis:vis $s:ident {$($name:ident $({$($rest:tt)*})?),* $(,)?}) => {
        $(
            impl_network_depth!(@impl #[$tgt] $vis $s $name $({$($rest)*})?);
        )*
    };
    (@impl #[$tgt:ident] $vis:vis enum $name:ident $({$($rest:tt)*})?) => {
        #[derive(Clone, Copy, Debug, Eq, Hash, Ord, PartialEq, PartialOrd)]
        #[cfg_attr(feature = "serde", derive(serde::Deserialize, serde::Serialize))]
        $vis enum $name {}

        impl $tgt for $name {
            seal!();

            $($($rest)*)?
        }
    };
}

impl_network_depth! {
    #[NetworkDepth]
    pub enum {
        Deep {
            fn is_deep(&self) -> bool {
                true
            }
        },
        Shallow,
    }
}

/*
 ************* Implementations *************
*/

impl<T> RawModelLayout for &T
where
    T: RawModelLayout,
{
    fn input(&self) -> usize {
        <T as RawModelLayout>::input(self)
    }
    fn hidden(&self) -> usize {
        <T as RawModelLayout>::hidden(self)
    }
    fn depth(&self) -> usize {
        <T as RawModelLayout>::depth(self)
    }
    fn output(&self) -> usize {
        <T as RawModelLayout>::output(self)
    }
}

impl<T> RawModelLayout for &mut T
where
    T: RawModelLayout,
{
    fn input(&self) -> usize {
        <T as RawModelLayout>::input(self)
    }
    fn hidden(&self) -> usize {
        <T as RawModelLayout>::hidden(self)
    }
    fn depth(&self) -> usize {
        <T as RawModelLayout>::depth(self)
    }
    fn output(&self) -> usize {
        <T as RawModelLayout>::output(self)
    }
}

impl<T> LayoutExt for T where T: RawModelLayoutMut + Copy + core::fmt::Debug {}

impl RawModelLayout for (usize, usize, usize) {
    fn input(&self) -> usize {
        self.0
    }
    fn hidden(&self) -> usize {
        self.1
    }
    fn depth(&self) -> usize {
        1
    }
    fn output(&self) -> usize {
        self.2
    }
}

impl RawModelLayout for (usize, usize, usize, usize) {
    fn input(&self) -> usize {
        self.0
    }
    fn hidden(&self) -> usize {
        self.1
    }
    fn output(&self) -> usize {
        self.2
    }

    fn depth(&self) -> usize {
        self.3
    }
}

impl RawModelLayoutMut for (usize, usize, usize, usize) {
    fn input_mut(&mut self) -> &mut usize {
        &mut self.0
    }

    fn hidden_mut(&mut self) -> &mut usize {
        &mut self.1
    }

    fn layers_mut(&mut self) -> &mut usize {
        &mut self.2
    }

    fn output_mut(&mut self) -> &mut usize {
        &mut self.3
    }
}

impl RawModelLayout for [usize; 3] {
    fn input(&self) -> usize {
        self[0]
    }

    fn hidden(&self) -> usize {
        self[1]
    }

    fn output(&self) -> usize {
        self[2]
    }

    fn depth(&self) -> usize {
        1
    }
}

impl RawModelLayout for [usize; 4] {
    fn input(&self) -> usize {
        self[0]
    }

    fn hidden(&self) -> usize {
        self[1]
    }

    fn output(&self) -> usize {
        self[2]
    }

    fn depth(&self) -> usize {
        self[3]
    }
}

impl RawModelLayoutMut for [usize; 4] {
    fn input_mut(&mut self) -> &mut usize {
        &mut self[0]
    }
    fn hidden_mut(&mut self) -> &mut usize {
        &mut self[1]
    }
    fn layers_mut(&mut self) -> &mut usize {
        &mut self[2]
    }
    fn output_mut(&mut self) -> &mut usize {
        &mut self[3]
    }
}